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Sift

AI-driven real-time fraud prevention for e-commerce

Overview

Sift builds a Digital Trust & Safety platform that helps e-commerce and fintech companies prevent fraud while keeping legitimate customers moving. It uses machine learning and AI to analyze vast streams of data in real time, delivering risk scores and decisions that integrate with clients’ existing systems. The platform provides dynamic friction—allowing valid transactions to proceed smoothly while stopping fraudulent ones—and it scales across small online retailers to large financial institutions. Revenue comes from a subscription model plus consulting and custom integration services. Sift’s goal is to reduce chargebacks and account takeovers, protect customer trust, and support safe digital commerce at scale, by delivering precise, real-time fraud prevention without disrupting normal user experiences.

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About Sift

Simplify's Rating
Why Sift is rated
B-
Rated B on Competitive Edge
Rated B on Growth Potential
Rated C on Differentiation

Industries

Data & AnalyticsFintechCybersecurityAI & Machine Learning

Company Size

201-500

Company Stage

Series E

Total Funding

$156.7M

Headquarters

San Francisco, California

Founded

2011

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Simplify's Take

What believers are saying

  • Sift’s Q2 2026 network grew transaction volume 15.2%, expanding training data and coverage.
  • G2 Fall 2026 ranked Sift a Leader, reinforcing enterprise credibility with buyers.
  • 2026 reports show 37.5 million attacks denied in 2025, supporting strong ROI narratives.

What critics are saying

  • Stripe, Adyen, and PayPal bundle fraud controls, squeezing Sift’s standalone attach rates in 2026.
  • Sift’s own Q2 2026 data shows Internet and Software ATO worsening, exposing vertical concentration.
  • Fraud rings crossed 90 businesses in 2026; one model failure can ripple across customers.

What makes Sift unique

  • Sift’s network spans 700+ brands and 1 trillion annual events, creating cross-merchant fraud intelligence.
  • Sift combines payment fraud, account defense, and abuse prevention in one configurable platform.
  • Sift’s specialists and workflows let fraud teams tune decisions without heavy engineering dependency.

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Funding

Total Funding

$156.7M

Below

Industry Average

Funded Over

7 Rounds

Series E funding typically includes additional rounds after Series D if the company needs more capital. The business is usually stable, and these rounds are typically used for further expansion or to address market challenges.
Series E Funding Comparison
Below Average

Industry standards

$100M
$245M
$250M
$1.3B
$1.5B

Benefits

  • Flexible Work Hours

Growth & Insights and Company News

Headcount

6 month growth

-4%

1 year growth

-2%

2 year growth

-3%
SpaceNews
Jun 25th, 2024
Sift raises $17.5M in Series A

Southern California startup Sift raised $17.5 million in a Series A funding round led by Google Ventures. Announced on June 25, the investment will help Sift expand its staff and accelerate the development of its platform, which aids engineers in analyzing hardware sensor data. Sift's automated data review product generates reports to highlight potential issues, benefiting industries like aviation, defense, energy, and transportation.

Globe Newswire
Nov 17th, 2021
Sift Acquires Passwordless Authentication Pioneer Keyless to Provide Regulated Businesses and Online Merchants with Secure, Frictionless Authentication

Biometric authentication innovator eliminates password-based account takeover and enables PSD2 Strong Customer Authentication while preserving user privacy

TechCrunch
Apr 22nd, 2021
Fraud prevention platform Sift raises $50M at over $1B valuation, eyes acquisitions

With the increase of digital transacting over the past year, cybercriminals have been having a field day.

PYMNTS
Oct 8th, 2020
The Ordering Innovations Attracting Restaurant Customers

This Deep Dive explores how restaurants are using innovative dining and ordering technology to satisfy customer needs amid the COVID-19 pandemic.

PYMNTS
Sep 22nd, 2020
Deep Dive: How QSRs Can Fight The Rise Of App-Enabled Friendly Fraud

This Deep Dive examines how the increased use of mobile apps has led to a rise in friendly fraud and how AI and machine learning can help fight it.

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